System and method for determining effectiveness of product promotions

ABSTRACT

This disclosure relates generally to analyzing product promotions, and more particularly to system and method for determining effectiveness of product promotions. In one embodiment, a method is provided for determining an effectiveness of a promotion. The method includes receiving data related to sales and promotions for a plurality of products. The plurality of products includes a plurality of aggressor products and a plurality of victim products. The method further includes analyzing the data to determine a set of significant aggressor products for each of the plurality of victim products, assessing an impact of each of the set of significant aggressor products on a respective victim product, and determining the effectiveness of the promotion with respect to a product based on the data related to sales of the product under the promotion, and the impact of the product on each of one or more associated victim products.

CROSS REFERENCE TO RELATED APPLICATION

This disclosure relates to co-pending U.S. patent application Ser. No.15/087,480 filed on Mar. 31, 2016, which claims priority to IndianPatent Application number 201641008601 filed on Mar. 11, 2016, bothentitled “System and Method for Generating Promotion Data”, and bothassigned to the same assignee as this application, the entirety of whichis hereby incorporated by reference herein.

This application claims the benefit of Indian Patent Application SerialNo. 201741023025, filed Jun. 30, 2017, which is hereby incorporated byreference in its entirety.

FIELD

This disclosure relates generally to product promotions, and moreparticularly to system and method for determining effectiveness ofproduct promotions.

BACKGROUND

In an increasingly competitive market, many new and different productsavailable in market, such as products in the consumer packaged goods(CPG) space, vie for consumer attention. To boost sales of theirproducts, companies typically run promotional campaigns. Promotions in apromotional campaign may include distributing sample products,promotional pricing, providing discounts, increasing product visibilityby strategic positioning in shops or by running television commercials,and so forth. Typically, these promotional campaigns have a directrelationship with sales of the products. However, many a times, theremay be interdependencies and it may therefore be difficult to analyzeand to accurately assess effectiveness of promotional campaigns.

For example, promotional campaign by a company to promote sales of oneof its products may not only cannibalize the sales of similar productsof its competitors, but may also cannibalize the sales of its othersimilar products. In some scenarios, a product may cannibalize sales ofthe other products of same brand. It is therefore imperative tounderstand such interdependencies for determining effectiveness of thepromotion.

SUMMARY

In one embodiment, a method for determining an effectiveness of apromotion is disclosed. In one example, the method includes receivingdata related to sales and promotions for a plurality of products fromone or more data sources. The plurality of products includes a pluralityof aggressor products and a plurality of victim products. The methodfurther includes analyzing the data to determine a set of significantaggressor products for each of the plurality of victim products. Themethod further includes assessing an impact of each of the set ofsignificant aggressor products on a respective victim product. Themethod further includes determining the effectiveness of the promotionwith respect to a product based on the data related to sales of theproduct under the promotion, and the impact of the product on each ofone or more associated victim products.

In one embodiment, a system for determining an effectiveness of apromotion is disclosed. In one example, the system includes at least oneprocessor and a memory communicatively coupled to the at least oneprocessor. The memory stores processor-executable instructions, which,on execution, cause the processor to receive data related to sales andpromotions for a plurality of products from one or more data sources.The plurality of products includes a plurality of aggressor products anda plurality of victim products. The processor-executable instructions,on execution, further cause the processor to analyze the data todetermine a set of significant aggressor products for each of theplurality of victim products. The processor-executable instructions, onexecution, further cause the processor to assess an impact of each ofthe set of significant aggressor products on a respective victimproduct. The processor-executable instructions, on execution, furthercause the processor to determine the effectiveness of the promotion withrespect to a product based on the data related to sales of the productunder the promotion, and the impact of the product on each of one ormore associated victim products.

In one embodiment, a non-transitory computer-readable medium storingcomputer-executable instructions for determining an effectiveness of apromotion is disclosed. In one example, the stored instructions, whenexecuted by a processor, cause the processor to perform operationsincluding receiving data related to sales and promotions for a pluralityof products from one or more data sources. The plurality of productsincludes a plurality of aggressor products and a plurality of victimproducts. The operations further include analyzing the data to determinea set of significant aggressor products for each of the plurality ofvictim products. The operations further include assessing an impact ofeach of the set of significant aggressor products on a respective victimproduct. The operations further include determining the effectiveness ofthe promotion with respect to a product based on the data related tosales of the product under the promotion, and the impact of the producton each of one or more associated victim products.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles.

FIG. 1 is a block diagram of an exemplary system for determiningpromotion effectiveness in accordance with some embodiments of thepresent disclosure.

FIG. 2 is a functional block diagram of a promotion effectivenessanalytics engine in accordance with some embodiments of the presentdisclosure.

FIG. 3 is a flow diagram of an exemplary process for determiningpromotion effectiveness in accordance with some embodiments of thepresent disclosure.

FIG. 4 is a flow diagram of a detailed exemplary process for determiningpromotion effectiveness in accordance with some embodiments of thepresent disclosure.

FIG. 5 is a block diagram of an exemplary computer system forimplementing embodiments consistent with the present disclosure.

DETAILED DESCRIPTION

Exemplary embodiments are described with reference to the accompanyingdrawings. Wherever convenient, the same reference numbers are usedthroughout the drawings to refer to the same or like parts. Whileexamples and features of disclosed principles are described herein,modifications, adaptations, and other implementations are possiblewithout departing from the spirit and scope of the disclosedembodiments. It is intended that the following detailed description beconsidered as exemplary only, with the true scope and spirit beingindicated by the following claims.

Referring now to FIG. 1, an exemplary system 100 for determiningpromotion effectiveness is illustrated in accordance with someembodiments of the present disclosure. In particular, the system 100implements a promotion effectiveness analytics engine to determineeffectiveness of product promotions. As will be described in greaterdetail in conjunction with FIG. 2, the promotion effectiveness analyticsengine receives data related to sales and promotions for a plurality ofproducts from one or more data sources. The plurality of productscomprises a plurality of aggressor products and a plurality of victimproducts. The promotion effectiveness analytics engine further analyzesthe data to determine a set of significant aggressor products for eachof the plurality of victim products, and assesses an impact of each ofthe set of significant aggressor products on a respective victimproduct. The promotion effectiveness analytics engine further determinesthe effectiveness of the promotion with respect to a product based onthe data related to sales of the product under the promotion, and theimpact of the product on each of one or more associated victim products.

The system 100 includes one or more processors 101, a computer-readablemedium (e.g., a memory) 102, and a display 103. The computer-readablemedium 102 stores instructions that, when executed by the one or moreprocessors 101, cause the one or more processors 101 to generate namedentities in accordance with aspects of the present disclosure. Thecomputer-readable medium 102 may also store various data (e.g., salesrevenue, sales volume, promotions, aggressor products, victim products,aggressor-victim combinations, correlation indices, multicollinearityindices, potential aggressors, significant aggressors, impactcoefficients, promotion effectiveness, metadata, etc.) that may bereceived, captured, required, processed, and/or generated by the system100. The system 100 interacts with a user via a user interface 104accessible via the display 103. The system 100 may also interact withone or more external devices 105 over a communication network 106 forsending or receiving various data. The external devices 105 may include,but are not limited to, a remote server, a digital device, or anothercomputing system.

Referring now to FIG. 2, a functional block diagram of the promotioneffectiveness analytics engine 200 implemented by the system 100 of FIG.1 is illustrated in accordance with some embodiments of the presentdisclosure. The promotion effectiveness analytics engine 200 may includevarious modules that perform various functions so as to determineeffectiveness of product promotions. In some embodiments, the promotioneffectiveness analytics engine 200 may include a data repository module201, a data filtering and harmonization module 202, a product (SKU)collection module 203, a product (SKU) selection module 204, ananalytics module 205, an impact assessment module 206, and a promotioneffectiveness determination module 207. The promotion effectivenessanalytics engine 200 may further include a database 208 for storingvarious data that may be received, captured, required, processed, and/orgenerated by various module 201-207. It should be noted that each of theproducts may be identified using a unique product identification code,also referred to as a stock keeping unit (SKU).

The data repository module 201 receives data related to sales andpromotions for a number of products from one or more data sources 209.In some embodiments, the data related to sales and promotions mayinclude sales revenue data, sales volume data, merchandising data,promotions, and so forth for each of the products. It should be notedthat the data may include data prior to, during, and after promotionalcampaigns comprising of one or more promotional events and may be usedfor pre-event analytics as well as for post event analytics. The one ormore data sources 209 may include, but are not limited to, a retailer(e.g., authorized retailers, multi-brand retailers, departmental store,supermarket, hypermarket, etc.), a supplier (e.g., a distributer, amanufacturer, etc.), a sales team (e.g., in-house sales team,distributers, channel partners, etc.), a third-party market researchorganization (e.g., A C NIELSEN, IRI, RSI, etc.), a real-time salesmanagement platform (e.g., RETAIL LINK, etc.). Thus, in someembodiments, the data repository module 201 may be adapted toincorporate syndicated data as well as retailer direct point of sale(PoS) data from data sources 209 such as A C NIELSEN, IRI, RETAIL LINK,RSI, and so forth. The data received by the data repository module 201may be stored in the database 208.

The products may include aggressor products as well as victim products.As will be appreciated, a product of a company that cannibalizes thesales of other similar products of the company, apart from similarproducts of one or more competitor of the company, is typically referredto as an aggressor product. The similar products whose sale iscannibalized by the aggressor product are typically referred to asvictim products. Thus, an aggressor is that product or SKU whichcannibalizes the sales, while a victim is that product or SKU whosesales get cannibalized (e.g., due to a promotion offered on theaggressor). In some embodiments, the aggressor products and the victimproducts are pre-identified by one or more data sources 209.Alternatively, in some embodiments, the aggressor products and thevictim products are identified or fed by a user through a userinterface. Thus, the user may identify or feed categories,sub-categories, product hierarchy for the products of the company aswell similar products of the competitors. For example, the data may becollected from one or more consumer packaged goods (CPG) clients for allproducts, and stored in the data repository module 201. An expert usermay then select all the victim-aggressor combinations from the collecteddata via the user interface.

The data received by the data repository module 201 may be in structuredformat or in unstructured format. The structured data may be in astandard format. However, the unstructured data may be in a formatdifferent from the standard format or may not be formatted at all. In anexample, unstructured data may include e-mail messages, word processingdocuments, images, presentations, webpages, etc. The data filtering andharmonization module 202 acquires the structured and the unstructureddata from the data repository module 201, and performs various dataprocessing operations to filter and harmonize the data into apre-defined format capable of being processed by subsequent modules203-207. The pre-defined format may have robust capabilities to captureand display various sales and promotion related data points. In someembodiments, the data filtering and harmonization module 202 may performharmonization using a master template, and incorporating both thestructured and unstructured data in the master template format.Additionally, the data filtering and harmonization module 202 mayperform various data processing operations to cleanse the data to ensurethat the data is complete (e.g., no missing records) and ready forsubsequent processing. The data filtering and harmonization module 202then provides the cleansed and harmonized data to the product (SKU)collection module 203. In some embodiments, the data filtering andharmonization module 202 may filter or prepare a single data set fromall the victim-aggressor combinations' data for analysis.

The product (SKU) collection module 203 acquires all victim-aggressorcombinations' data from the data filtering and harmonization module 202,thereby creating a harmonized superset data for analysis. The product(SKU) collection module 203 then provides all victim-aggressorcombinations' data to the product (SKU) selection module 204 for furtheranalysis. The product (SKU) selection module 204 analyzes allvictim-aggressor combinations' data to determine potential aggressorproducts for each of the victim products. In some embodiments, theproduct (SKU) selection module 204 performs analysis by screening thedata to determine the potential aggressor products for each of thevictim products from among all aggressor products. In some embodiments,the product (SKU) selection module 204 screens the data by deriving acorrelation index between each of the aggressor products and each of thevictim products, and then selects the potential aggressor products basedon the corresponding correlation indices. It should be noted that, insome embodiments, the correlation may be determined using collinearityor multicollinearity. In particular, multicollinearity may be employedas there may be multiple independent variables in the victim-aggressorcombinations' data. Thus, in such embodiments, the selection of thepotential aggressor products for a given victim product may be based onthe multicollinearity indices between the aggressor products and thegiven victim product. For example, the product (SKU) selection module204 may select those aggressor products as potential aggressor productsthat have multicollinearity indices greater than a pre-defined thresholdvalue (e.g., greater than 0.6), or that meet a pre-defined conditionbased on their multicollinearity indices (e.g., in top 20% or in top40). Thus, the product (SKU) selection module 204 filters potentialaggressors for every victim-aggressor combination. The potentialaggressors may then be provided to the analytics module 205.

The analytics module 205 receives potential aggressors for each victimfrom the product (SKU) selection module 204. The analytics module 205then determines significant aggressors for a given victim from among thepotential aggressors for the given victim. In some embodiments, theanalytics module 205 determines significant aggressors from among theplurality of potential aggressors using back-step filtering multivariateregression algorithm. Further, the analytics module 205 provides thesignificant aggressors for a victim product to the user through the userinterface. Additionally, in some embodiments, the analytics module 205may estimate impact coefficients for each of the significant aggressorson a respective victim using the back-step filtering multivariateregression algorithm. The analytics module 205 may then provide theimpact coefficients to the impact assessment module 206.

The impact assessment module 206 assesses an impact of each of thesignificant aggressor products on a respective victim product. In someembodiments, the impact assessment module 206 may estimate the impact ofsignificant aggressors on a victim based on the impact coefficients. Insome embodiments, the impact coefficients may be cannibalizationcoefficients, and the impact assessment module 206 estimatescannibalization impact of significant aggressors on a victim based onthe cannibalization coefficients. The impact assessment module 206 maythen provide the impact of significant aggressors on a victim to thepromotion effectiveness determination module 207.

The promotion effectiveness determination module 207 receives the impactof significant aggressors on a victim from the impact assessment module206. The promotion effectiveness determination module 207 also receivesdata related to sales of the product under the promotion from theproduct (SKU) collection module 203. The promotion effectivenessdetermination module 207 then determines effectiveness of the promotionwith respect to a product based on the data related to sales of theproduct under the promotion, and the impact of the product on each ofone or more associated victim products. As will be appreciated, it isimperative to understand the significant victims for an aggressor on apromotion, and quantify the sales loss for associated victims, whichthen needs to be incorporated for determining effectiveness of thepromotion. It should be noted that the promotion effectiveness analyticsengine 200 starts with victims as the input parameter (i.e., identifyingsignificant aggressors for each of the victims, and assessing impact ofeach of the significant aggressors), but then the output is translatedfor the aggressor (i.e., determining effectiveness of the promotion foran aggressor based on its impact on victims).

As will be appreciated, most of the companies have requirement tounderstand the outcome of trade promotions in terms of cannibalizationwith regard to their products and also products from competitors. Theoutput of the impact assessment module 206 and the promotioneffectiveness determination module 207 may therefore help the companiesdesign promotions for their products by providing clear businessintelligence of aggressors' behavior and helps in revenue enhancement orfinancial reconciliation. For example, significant aggressors arehighlighted against a selected victim which helps the client withprecise business intelligence to design plan and design productpromotions in the future. In some embodiments, the model definitions andmodel coefficients generated by the modules 203-207 may be converted asmetadata, and stored in the database 208 for every victim-aggressorrelationship. The metadata generated and stored may be then used forfuture reference. The saved model is particularly useful with respect todynamic changes in victim-aggressor relationships.

It should be noted that the promotion effectiveness analytics engine 200may be implemented in programmable hardware devices such as programmablegate arrays, programmable array logic, programmable logic devices, andso forth. Alternatively, the promotion effectiveness analytics engine200 may be implemented in software for execution by various types ofprocessors. An identified engine of executable code may, for instance,include one or more physical or logical blocks of computer instructionswhich may, for instance, be organized as an object, procedure, function,module, or other construct. Nevertheless, the executables of anidentified engine need not be physically located together, but mayinclude disparate instructions stored in different locations which, whenjoined logically together, include the engine and achieve the statedpurpose of the engine. Indeed, an engine of executable code could be asingle instruction, or many instructions, and may even be distributedover several different code segments, among different applications, andacross several memory devices.

As will be appreciated by one skilled in the art, a variety of processesmay be employed for determining effectiveness of product promotions. Forexample, the exemplary system 100 and the associated promotioneffectiveness analytics engine 200 may determine an effectiveness of aproduct promotion by the processes discussed herein. In particular, aswill be appreciated by those of ordinary skill in the art, control logicand/or automated routines for performing the techniques and stepsdescribed herein may be implemented by the system 100 and the associatedpromotion effectiveness analytics engine 200, either by hardware,software, or combinations of hardware and software. For example,suitable code may be accessed and executed by the one or more processorson the system 100 to perform some or all of the techniques describedherein. Similarly, application specific integrated circuits (ASICs)configured to perform some or all of the processes described herein maybe included in the one or more processors on the system 100.

For example, referring now to FIG. 3, exemplary control logic 300 fordetermining an effectiveness of a promotion via a system, such as system100, is depicted via a flowchart in accordance with some embodiments ofthe present disclosure. As illustrated in the flowchart, the controllogic 300 includes the step of receiving data related to sales andpromotions for a plurality of products from one or more data sources atstep 301. It should be noted that the plurality of products includes aplurality of aggressor products and a plurality of victim products. Thecontrol logic 300 further includes the steps of analyzing the data todetermine a set of significant aggressor products for each of theplurality of victim products at step 302, and assessing an impact ofeach of the set of significant aggressor products on a respective victimproduct at step 303. The control logic 300 further includes the step ofdetermining the effectiveness of the promotion with respect to a productbased on the data related to sales of the product under the promotion,and the impact of the product on each of one or more associated victimproducts at step 304.

In some embodiments, the data related to sales and promotions comprisesat least one of sales revenue data, sales volume data, and merchandisingdata prior to, during, and after one or more promotional events for eachof the plurality of products. Additionally, in some embodiments, the oneor more data sources comprises at least one of: a retailer, a supplier,a sales team, a third-party market research organization, a real-timesales management platform. Further, in some embodiments, the pluralityof aggressor products and the plurality of victim products areidentified or fed by a user.

In some embodiments, analyzing the data to determine the set ofsignificant aggressor products at step 302 includes screening the datato determine a plurality of potential aggressor products for each of theplurality of victim products, and determining the set of significantaggressor products from among the plurality of potential aggressorproducts. Additionally, in some embodiments, screening the data includesderiving a multicollinearity index between each of the plurality ofaggressor products and each of the plurality of victim products, andselecting the plurality of potential aggressor products from among theplurality of aggressor products based on the correspondingmulticollinearity indices. Further, in some embodiments, determining theset of significant aggressor products includes determining the set ofsignificant aggressor products from among the plurality of potentialaggressor products using back-step filtering multivariate regression.

In some embodiments, assessing the impact at step 303 includesestimating impact coefficients using back-step filtering multivariateregression. Additionally, in some embodiments, the control logic 300includes the steps of converting the impact of each of the set ofsignificant aggressor products on a respective victim product asmetadata, and storing the metadata for subsequent use.

Referring now to FIG. 4, exemplary control logic 400 for determiningeffectiveness of product promotions is depicted in greater detail via aflowchart in accordance with some embodiments of the present disclosure.As illustrated in the flowchart, the control logic 400 includes thesteps of collecting victims and aggressors data at step 401, determininga baseline and an uplift volume of each aggressors at step 402,determining potential aggressors using multicollinearity indices at step403, determining significant aggressors and estimating impact (e.g.,cannibalization) coefficients for the significant aggressors usingback-step filtering multivariate regression at step 404, assessing animpact (e.g., cannibalization) on victim by the significant aggressorsat step 405, and determining effectiveness of promotion based on theimpact (e.g., cannibalization) at step 406. Each of these steps will bedescribed in greater detail below.

At step 401, the data repository module 201 collects the data from user(e.g., CPG clients) and/or data sources 209 on a periodic (e.g., weekly,monthly, etc.) basis. The data may broadlly be historical time seriesdata on the promotion and non promotion performance of the aggressorsand the victims respectively. This paves way for defining thevictim-aggressor relationship. Upon data collection, the data filteringand harmonization module 202 subjects the data to harmonization. Theharmonized data may then be imported for screening and cleansing. Anymissing aggressor values may be initialized to zero.

At step 402, the product (SKU) selection module 204 determines abaseline volume and an uplift volume (i.e., increase in the volume salesover the baseline volume while on promotion) of each aggressors using aset of quantitative methodologies. The process is repeated continuouslyfor all victim-aggressor combinations. It should be noted that a productor SKU that is a victim may be an aggressor while on promotion foranother product or SKU. Some of the quantitative methodologies forcalculating baseline and uplift volume may be found in co-pending IndianPatent Application No. 201641008601 filed on Mar. 11, 2016 andco-pending U.S. patent application Ser. No. 15/087,480 filed on Mar. 31,2016, both entitled “System and Method for Generating Promotion Data”,and both assigned to the same assignee as this application, the entiretyof which is hereby incorporated by reference herein.

At step 403, upon calculation of the the baseline and uplift volume forall the victim-aggressor combinations, the product (SKU) selectionmodule 204 identifies potential aggressors for each of thevictim-aggressor combinations using multi-step regression technique. Thefirst part of the multi-step regression technique derives amulticollinearity index between each of the aggressor products and eachof the victim products. The potential aggressors are then identified andselected from the superset of aggressors based on theirmulticollinearity indices. For example, if there are 200 data points inthe dataset, the process filters no more than the top 20% of 200 (i.e.,40 datapoints) as potential aggressors by deriving the multicollinearityindices. In some embodiments, the process may also rank the filteredpotential aggressors based on their multicollinearity indices.

As will be appreciated, the multicollinearity indices signify the extentof the relationship between aggressors and the associated victims. Themulticollinearity index may be in the range of −1 and +1. The higher theabsolute value of the multicollinearity index (e.g., 0.85), the highercorrelation the aggressor has with the victim; hence such aggressor mayhave higher impact on the victim and may therefore be identified as apotential aggressor. On the other hand, the lower the absolute value ofthe multicollinearity index (e.g., 0.15), the lower correlation theaggressor has with the victim; hence such aggressor may have limited orno impact on the victim and may therefore not be identified as apotential aggressor. The selection of potential aggressors ensuresquantitative stability so that the model does not crash if all thevictim-aggressor combinations are subjected to analysis. In other words,if the number of aggressors are substantially higher as compared to thenumber of data points available for analysis, there exists a highpossibility that the model may be overfitted or unduly sensitive tochanges in the aggressors.

At step 404, the analytics module 205 determines significant aggressorsfrom among the potential aggressors, and estimates impact (e.g.,cannibalization) coefficients for significant aggressors using back-stepfiltering multivariate regression algorithm. The set of potentialaggressors identified at step 403 are subjected to an automatedback-step filtering multivariate regression algorithm that filterspotential aggressors based on multiple statistical criteria such as theAkaike information criterion (AIC) and the p,t values. The algorithmiteratively works in the backward direction under the assumption thatall the potential aggressors are significant, until the most significantare retained and less significant are filtered. This results inidentifying the significant aggressors and their impact (e.g.,cannibalization) coefficients, and therefore enables estimating theextent of impact (e.g., cannibalization) of sales of a victim. By way ofan example, the functional form of the model for determining significantaggressors and their impact coefficients may be represented by a set ofequations provided below:

$\quad\begin{matrix}{{{Victim}\mspace{14mu} {Scan}\mspace{14mu} {Volume}\mspace{14mu} (Y)} =} \\{\alpha + {{Uplift}\mspace{14mu} {Volume}\mspace{14mu} {of}\mspace{14mu} {{Aggressors}(\beta)}_{{1\mspace{14mu}...}\mspace{14mu} N}\left( X_{{1\mspace{14mu}...}\mspace{20mu} N} \right)} + {ɛ\mspace{14mu} \ldots}} \\{{Fit}\mspace{14mu} 1\mspace{14mu} {that}\mspace{14mu} {includes}\mspace{14mu} {all}\mspace{14mu} {potential}\mspace{14mu} {aggressors}} \\{\vdots\vdots} \\{\vdots\vdots} \\{{{Victim}\mspace{14mu} {Scan}\mspace{14mu} {Volume}\mspace{14mu} (Y)} = {\alpha + {\beta_{1}X_{1}} + {\beta_{4}X_{4}} + \ldots \mspace{14mu} + {\beta_{N - 1}X_{N - 1}} + {ɛ\mspace{14mu} \ldots}}} \\{{Fit}\mspace{14mu} 2\mspace{14mu} {that}\mspace{14mu} {filters}\mspace{14mu} {non}\text{-}{significant}\mspace{14mu} {aggressors}}\end{matrix}$

At the first step of back-filtering of aggressors, all the potentialaggressors may be assigned equal importance. The model may then test thedeletion of every single variable in a way that such deletion does notsignificantly affect the model fit or robustness. As per a pre-specifiedconfidence interval estimates (e.g., 90%, 95%, etc.), the model may workin the backward direction until it finds the significant aggressorswhich may be estimated at the pre-specified confidence levels. It may bepossible that there may be four aggressors that are significant (e.g.,significant cannibalization candidates) for a victim, whereas there maybe eight or more aggressors that may be significant for another victim.The model accuracy may be measured by the mutiple R squared value of thestep regression, and the mean absolute percentage error (MAPE) which maybe calculated as the percentage difference between the actual andpredicted values.

At step 405, the impact assessment module 206 assesses the impact (e.g.,cannibalization impact) on a victim by the associated significantaggressors. The impact coefficients (e.g., cannibalization coefficients)derived at step 404 represent both the individual and collective impacton the victim. The summation of the filtered aggressor impactcoefficients in conjunction with the respective aggressor uplift volumesdetermines the extent of impact on the victim by the significantaggressors. By way of an example, the impact on the victim (i.e., changein the victim volume caused by the aggressors) and the total victimvolume after the impact may be represented by equations provided below:

Impact_(victim)=Σ_((β-imp Coeff) _(sum) )×Uplift_(Aggressor) _(sum)

TotalVolume_(victim)=α(regression intercept)+Σ_((β-imp Coeff) _(sum))×Uplift_(Agressor) _(sum) +ϵ(error term)

The beta (β) value represents the impact coefficient of that respectiveaggressors with the victim. The value may be either positive ornegative. If the (β) value is negative, it means that any promotiongiven on that respective aggressor negatively impacts (i.e.,cannibalizes) the sales of the victim. For example, if (β=−0.6), itindicates if there is an uplift of the aggressor's volume by 1 unit, thevictim's sales get cannibalized by 0.6 units. However, if the (β) valueis positive, it means that any promotion given on that respectiveaggressor positively impacts (i.e., promotes) the sales of the victim.For example, if (β=0.85), it indicates that when the aggressor ispromoted, the victim's sales are not cannibalized but increase by 0.85units with every unit increase of the aggressor.

It should be noted that the steps 402-405 may be repeated forinterchangeable victim-aggressor combinations. At step 406, thepromotion effectiveness determination module 207 determineseffectiveness of the promotion based on the impact. The processtherefore determines effective promotional campaigns for the clientbased on the impact assessment. As will be appreciated, the many-manyvictim-aggressor mapping by promotion effectiveness determination module207 provides the client a robust platform for advanced promotiondesigning and planning. For example, the client may experiment withvariation in timing of promotional campaigns (e.g., summer promotionsrather than spring promotions in the Australian and European markets,Christmas promotions rather than Thanksgiving day promotions in the USmarket, etc.). By way of example, if the cannibalizaton coefficient ofan aggressor is negative, the client would have better businessintelligence to avoid promotion during spring but instead tag theaggressor for promotion during summer. Similarly, by way of example, forthe US market, if the cannibalization coefficient is positive, thiswould help in the client in deciding promotions during Christmas.Further, as will be appreciated, such intelligence for promotiondesigning and planning may result in reduction in expenditure on lessimpactful promotional campaigns and advertisements, and boost revenue bychoosing the right time and the right place of promotions as per marketinformation.

As will be also appreciated, the above described techniques may take theform of computer or controller implemented processes and apparatuses forpracticing those processes. The disclosure can also be embodied in theform of computer program code containing instructions embodied intangible media, such as floppy diskettes, CD-ROMs, hard drives, or anyother computer-readable storage medium, wherein, when the computerprogram code is loaded into and executed by a computer or controller,the computer becomes an apparatus for practicing the invention. Thedisclosure may also be embodied in the form of computer program code orsignal, for example, whether stored in a storage medium, loaded intoand/or executed by a computer or controller, or transmitted over sometransmission medium, such as over electrical wiring or cabling, throughfiber optics, or via electromagnetic radiation, wherein, when thecomputer program code is loaded into and executed by a computer, thecomputer becomes an apparatus for practicing the invention. Whenimplemented on a general-purpose microprocessor, the computer programcode segments configure the microprocessor to create specific logiccircuits.

The disclosed methods and systems may be implemented on a conventionalor a general-purpose computer system, such as a personal computer (PC)or server computer. Referring now to FIG. 5, a block diagram of anexemplary computer system 501 for implementing embodiments consistentwith the present disclosure is illustrated. Variations of computersystem 501 may be used for implementing system 100 and promotioneffectiveness analytics engine 200 for determining effectiveness ofproduct promotions. Computer system 501 may include a central processingunit (“CPU” or “processor”) 502. Processor 502 may include at least onedata processor for executing program components for executinguser-generated or system-generated requests. A user may include aperson, a person using a device such as those included in thisdisclosure, or such a device itself. The processor may includespecialized processing units such as integrated system (bus)controllers, memory management control units, floating point units,graphics processing units, digital signal processing units, etc. Theprocessor may include a microprocessor, such as AMD Athlon, Duron orOpteron, ARM's application, embedded or secure processors, IBM PowerPC,Intel's Core, Itanium, Xeon, Celeron or other line of processors, etc.The processor 502 may be implemented using mainframe, distributedprocessor, multi-core, parallel, grid, or other architectures. Someembodiments may utilize embedded technologies like application-specificintegrated circuits (ASICs), digital signal processors (DSPs), FieldProgrammable Gate Arrays (FPGAs), etc.

Processor 502 may be disposed in communication with one or moreinput/output (I/O) devices via I/O interface 503. The I/O interface 503may employ communication protocols/methods such as, without limitation,audio, analog, digital, monoaural, RCA, stereo, IEEE-1394, serial bus,universal serial bus (USB), infrared, PS/2, BNC, coaxial, component,composite, digital visual interface (DVI), high-definition multimediainterface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x,Bluetooth, cellular (e.g., code-division multiple access (CDMA),high-speed packet access (HSPA+), global system for mobilecommunications (GSM), long-term evolution (LTE), WiMax, or the like),etc.

Using the I/O interface 503, the computer system 501 may communicatewith one or more I/O devices. For example, the input device 504 may bean antenna, keyboard, mouse, joystick, (infrared) remote control,camera, card reader, fax machine, dongle, biometric reader, microphone,touch screen, touchpad, trackball, sensor (e.g., accelerometer, lightsensor, GPS, gyroscope, proximity sensor, or the like), stylus, scanner,storage device, transceiver, video device/source, visors, etc. Outputdevice 505 may be a printer, fax machine, video display (e.g., cathoderay WL1283, Broadcom BCM4750IUB8, Infineon Technologies X-Gold618-PMB9800, or the like), providing IEEE 802.11a/b/g/n, Bluetooth, FM,global positioning system (GPS), 2G/3G HSDPA/HSUPA communications, etc.

In some embodiments, the processor 502 may be disposed in communicationwith a communication network 508 via a network interface 507. Thenetwork interface 507 may communicate with the communication network508. The network interface may employ connection protocols including,without limitation, direct connect, Ethernet (e.g., twisted pair10/100/1000 Base T), transmission control protocol/internet protocol(TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communicationnetwork 508 may include, without limitation, a direct interconnection,local area network (LAN), wide area network (WAN), wireless network(e.g., using Wireless Application Protocol), the Internet, etc. Usingthe network interface 507 and the communication network 508, thecomputer system 501 may communicate with devices 509, 510, and 511.These devices may include, without limitation, personal computer(s),server(s), fax machines, printers, scanners, various mobile devices suchas cellular telephones, smartphones (e.g., Apple iPhone, Blackberry,Android-based phones, etc.), tablet computers, eBook readers (AmazonKindle, Nook, etc.), laptop computers, notebooks, gaming consoles(Microsoft Xbox, Nintendo DS, Sony PlayStation, etc.), or the like. Insome embodiments, the computer system 501 may itself embody one or moreof these devices.

In some embodiments, the processor 502 may be disposed in communicationwith one or more memory devices (e.g., RAM 513, ROM 514, etc.),collectively referred to as memory 515, via a storage interface 512. Thestorage interface 512 may connect to memory devices including, withoutlimitation, memory drives, removable disc drives, etc., employingconnection protocols such as serial advanced technology attachment(SATA), integrated drive electronics (IDE), IEEE-1394, universal serialbus (USB), fiber channel, small computer systems interface (SCSI), etc.The memory drives may further include a drum, magnetic disc drive,magneto-optical drive, optical drive, redundant array of independentdiscs (RAID), solid-state memory devices, solid-state drives, etc.

The memory devices 515 may store a collection of program or databasecomponents, including, without limitation, an operating system 516, userinterface application 517, web browser 518, mail server 519, mail client520, user/application data 521 (e.g., any data variables or data recordsdiscussed in this disclosure), etc. The operating system 516 mayfacilitate resource management and operation of the computer system 501.Examples of operating systems include, without limitation, AppleMacintosh OS X, Unix, Unix-like system distributions (e.g., BerkeleySoftware Distribution (BSD), FreeBSD, NetBSD, OpenBSD, etc.), Linuxdistributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), Microsoft Windows(XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry OS, or thelike. User interface 517 may facilitate display, execution, interaction,manipulation, or operation of program components through textual orgraphical facilities. For example, user interfaces may provide computerinteraction interface elements on a display system operatively connectedto the computer system 501, such as cursors, icons, check boxes, menus,scrollers, windows, widgets, etc. Graphical user interfaces (GUIs) maybe employed, including, without limitation, Apple Macintosh operatingsystems' Aqua, IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.),Unix X-Windows, web interface libraries (e.g., ActiveX, Java,Javascript, AJAX, HTML, Adobe Flash, etc.), or the like.

In some embodiments, the computer system 501 may implement a web browser518 stored program component. The web browser may be a hypertext viewingapplication, such as Microsoft Internet Explorer, Google Chrome, MozillaFirefox, Apple Safari, etc. Secure web browsing may be provided usingHTTPS (secure hypertext transport protocol), secure sockets layer (SSL),Transport Layer Security (TLS), etc. Web browsers may utilize facilitiessuch as AJAX, DHTML, Adobe Flash, JavaScript, Java, applicationprogramming interfaces (APIs), etc. In some embodiments, the computersystem 501 may implement a mail server 519 stored program component. Themail server may be an Internet mail server such as Microsoft Exchange,or the like. The mail server may utilize facilities such as ASP,ActiveX, ANSI C++/C#, Microsoft .NET, CGI scripts, Java, JavaScript,PERL, PHP, Python, WebObjects, etc. The mail server may utilizecommunication protocols such as internet message access protocol (IMAP),messaging application programming interface (MAPI), Microsoft Exchange,post office protocol (POP), simple mail transfer protocol (SMTP), or thelike. In some embodiments, the computer system 501 may implement a mailclient 520 stored program component. The mail client may be a mailviewing application, such as Apple Mail, Microsoft Entourage, MicrosoftOutlook, Mozilla Thunderbird, etc.

In some embodiments, computer system 501 may store user/application data521, such as the data, variables, records, etc. (e.g., sales revenue,sales volume, promotions, aggressor products, victim products,aggressor-victim combinations, correlation indices, multicollinearityindices, potential aggressors, significant aggressors, impactcoefficients, promotion effectiveness, metadata, and so forth) asdescribed in this disclosure. Such databases may be implemented asfault-tolerant, relational, scalable, secure databases such as Oracle orSybase. Alternatively, such databases may be implemented usingstandardized data structures, such as an array, hash, linked list,struct, structured text file (e.g., XML), table, or as object-orienteddatabases (e.g., using ObjectStore, Poet, Zope, NoSQL, etc.). Suchdatabases may be consolidated or distributed, sometimes among thevarious computer systems discussed above in this disclosure. It is to beunderstood that the structure and operation of any computer or databasecomponent may be combined, consolidated, or distributed in any workingcombination.

As will be appreciated by those skilled in the art, the techniquesdescribed in the various embodiments discussed above provide forquantitative assessment of impact of aggressors on a victim. Thetechniques described in the embodiments discussed above model multipleaggressors and identify significant subsets with minimal manualintervention. The techniques described above then predict impact of eachof the significant aggressors on a victim with a high degree ofaccuracy. The techniques therefore enable the companies to pick andchoose or play with their promotional campaigns with caution, and avoidmistakes of overdoing with their promotions for the absence of reliablemarket information.

For example, the techniques described in the various embodimentsdiscussed above help the clients by identifying victim products (theirown products as well as that of competitors) for a product on promotion.Additionally, the techniques help in understanding and quantifying theimpact of a particular aggressor product under promotion on variousvictim products. Further, the techniques help in evaluatingeffectiveness of a completed promotion based on the impact (i.e., own aswell as cross-category cannibalization), thereby enabling a morerealistic assessment of return on investment (ROI). Moreover, thetechniques help in understanding the impact of competitor promotions onown product (i.e., competitor product modeled as aggressor, and ownproducts as victims). In other words, the techniques described in theembodiments discussed above help in identifying and evaluating theimpact of deep promotions on the competitor product, quantifying thesteal from competitor products, as well as in identifying, evaluating,and quantifying the impact of competitor promotions on own products. Thetechniques therefore enable designing and planning of future promotionsusing the impact coefficients and assessment. A demand planner may usethe impact coefficients derived at the product level for planning futurepromotion by removing the negative promotion (i.e., yielding negative orlimited ROI) and focusing only on the positive promotions (i.e.,yielding good ROI).

The specification has described system and method for determiningeffectiveness of product promotions. The illustrated steps are set outto explain the exemplary embodiments shown, and it should be anticipatedthat ongoing technological development will change the manner in whichparticular functions are performed. These examples are presented hereinfor purposes of illustration, and not limitation. Further, theboundaries of the functional building blocks have been arbitrarilydefined herein for the convenience of the description. Alternativeboundaries can be defined so long as the specified functions andrelationships thereof are appropriately performed. Alternatives(including equivalents, extensions, variations, deviations, etc., ofthose described herein) will be apparent to persons skilled in therelevant art(s) based on the teachings contained herein. Suchalternatives fall within the scope and spirit of the disclosedembodiments.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope and spirit of disclosed embodimentsbeing indicated by the following claims.

What is claimed is:
 1. A method for determining an effectiveness of apromotion, the method comprising: receiving, by a promotioneffectiveness analytics engine, data related to sales and promotions fora plurality of products from one or more data sources, wherein theplurality of products comprises a plurality of aggressor products and aplurality of victim products; analyzing, by the promotion effectivenessanalytics engine, the data to determine a set of significant aggressorproducts for each of the plurality of victim products; assessing, by thepromotion effectiveness analytics engine, an impact of each of the setof significant aggressor products on a respective victim product;determining, by the promotion effectiveness analytics engine, theeffectiveness of the promotion with respect to a product based on thedata related to sales of the product under the promotion, and the impactof the product on each of one or more associated victim products.
 2. Themethod of claim 1, wherein the data related to sales and promotionscomprises at least one of: sales revenue data, sales volume data, ormerchandising data prior to, during, and after one or more promotionalevents for each of the plurality of products.
 3. The method of claim 1,wherein the one or more data sources comprises at least one of: aretailer; a supplier; a sales team; a third-party market researchorganization; or a real-time sales management platform.
 4. The method ofclaim 1, wherein the plurality of aggressor products and the pluralityof victim products are identified or fed by a user.
 5. The method ofclaim 1, wherein the analyzing the data to determine the set ofsignificant aggressor products comprises: screening the data todetermine a plurality of potential aggressor products for each of theplurality of victim products; and determining the set of significantaggressor products from among the plurality of potential aggressorproducts.
 6. The method of claim 5, wherein the screening the datacomprises: deriving a multicollinearity index between each of theplurality of aggressor products and each of the plurality of victimproducts; and selecting the plurality of potential aggressor productsfrom among the plurality of aggressor products based on thecorresponding multicollinearity indices.
 7. The method of claim 5,wherein the determining the set of significant aggressor productscomprises determining the set of significant aggressor products fromamong the plurality of potential aggressor products using back-stepfiltering multivariate regression.
 8. The method of claim 1, wherein theassessing the impact comprises estimating impact coefficients usingback-step filtering multivariate regression.
 9. The method of claim 1,further comprising converting the impact of each of the set ofsignificant aggressor products on a respective victim product asmetadata, and storing the metadata for subsequent use.
 10. A system fordetermining an effectiveness of a promotion, the system comprising: atleast one processor; and a computer-readable medium storing instructionsthat, when executed by the at least one processor, cause the at leastone processor to perform operations comprising: receiving data relatedto sales and promotions for a plurality of products from one or moredata sources, wherein the plurality of products comprises a plurality ofaggressor products and a plurality of victim products; analyzing thedata to determine a set of significant aggressor products for each ofthe plurality of victim products; assessing an impact of each of the setof significant aggressor products on a respective victim product; anddetermining the effectiveness of the promotion with respect to a productbased on the data related to sales of the product under the promotion,and the impact of the product on each of one or more associated victimproducts.
 11. The system of claim 10, wherein the analyzing the data todetermine the set of significant aggressor products comprises: screeningthe data to determine a plurality of potential aggressor products foreach of the plurality of victim products; and determining the set ofsignificant aggressor products from among the plurality of potentialaggressor products.
 12. The system of claim 11, wherein the screeningthe data comprises: deriving a multicollinearity index between each ofthe plurality of aggressor products and each of the plurality of victimproducts; selecting the plurality of potential aggressor products fromamong the plurality of aggressor products based on the correspondingmulticollinearity indices.
 13. The system of claim 11, wherein thedetermining the set of significant aggressor products comprisesdetermining the set of significant aggressor products from among theplurality of potential aggressor products using back-step filteringmultivariate regression.
 14. The system of claim 10, wherein theassessing the impact comprises estimating impact coefficients usingback-step filtering multivariate regression.
 15. The system of claim 10,wherein the operations further comprise converting the impact of each ofthe set of significant aggressor products on a respective victim productas metadata, and storing the metadata for subsequent use.
 16. Anon-transitory computer-readable medium storing computer-executableinstructions for: receiving data related to sales and promotions for aplurality of products from one or more data sources, wherein theplurality of products comprises a plurality of aggressor products and aplurality of victim products; analyzing the data to determine a set ofsignificant aggressor products for each of the plurality of victimproducts; assessing an impact of each of the set of significantaggressor products on a respective victim product; and determining theeffectiveness of the promotion with respect to a product based on thedata related to sales of the product under the promotion, and the impactof the product on each of one or more associated victim products. 17.The non-transitory computer-readable medium of claim 16, wherein theanalyzing the data to determine the set of significant aggressorproducts comprises: screening the data to determine a plurality ofpotential aggressor products for each of the plurality of victimproducts; and determining the set of significant aggressor products fromamong the plurality of potential aggressor products.
 18. Thenon-transitory computer-readable medium of claim 17, wherein thescreening the data comprises: deriving a multicollinearity index betweeneach of the plurality of aggressor products and each of the plurality ofvictim products; and selecting the plurality of potential aggressorproducts from among the plurality of aggressor products based on thecorresponding multicollinearity indices.
 19. The non-transitorycomputer-readable medium of claim 17, wherein the determining the set ofsignificant aggressor products comprises determining the set ofsignificant aggressor products from among the plurality of potentialaggressor products using back-step filtering multivariate regression.20. The non-transitory computer-readable medium of claim 16, wherein theassessing the impact comprises estimating impact coefficients usingback-step filtering multivariate regression.